image sequence – Japanese Translation – Keybot Dictionary

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  2 Hits samuellaflamme.com  
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Imagick::getImageBlob — Returns the image sequence as a blob
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Imagick::getImageBlob — 画像シーケンスを blob で返す
  4 Hits controverses.org  
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Imagick::getImageBlob — Returns the image sequence as a blob
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Imagick::cropThumbnailImage — 切り取ってサムネイルを作成する
  9 Hits www.kindersmile.gr  
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Import an image sequence
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画像シークエンスのインポート
  www.quark.com  
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Create an image sequence to animate a logo
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アニメーションロゴイメージシーケンスの作成
  3 Hits cookingtiki.com  
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Imagick::getImageBlob — Returns the image sequence as a blob
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Imagick::getImageBlob — 画像シーケンスを blob で返す
  www.hci.iis.u-tokyo.ac.jp  
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Yoichi Sato, " Object shape and reflectance modeling from color image sequence ," Technical Report (Ph.D. Thesis), CMU-RI-TR-97-06, The Robotics Institute, Carnegie Mellon University, January 1997.
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Yoichi Sato, Imari Sato, and Katsushi Ikeuchi, " Object shape morphing with intermediate reflectance properties "
  2 Hits help.nchsoftware.com  
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Alternatively, to save all frames of a sequence as a series of images, see Image Sequence in Saving, Sharing, or Burning a Video.
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シーケンスの全てのフレームを一連の画像として保存することもできます。詳しくは画像シーケンスの項目を保存、共有、ディスク書き込みのページでご覧ください。
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Image sequence
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お問い合わせ
  er-web.jmk.ynu.ac.jp  
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Lung Region Segmentation from MR Image Sequence Using Active Contour Referring to Multiple Models
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（Reiko Minamikawa-Tachino, Noriko Kabuyama, Toshiyuki Gotoh, Seiichiro Kagei,et al.）
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Yoshihiro Watanabe, Hiroki Shibayama, and Masatoshi Ishikawa: Reconstruction of 3D Surface and Restoration of Flat Document Imagefrom Monocular Image Sequence, IEICE Trans. on Information and Systems, Vol.
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Yoshihiro Watanabe, Takashi Komuro, Shingo Kagami, and Masatoshi Ishikawa: Multi-Target Tracking Using a Vision Chip and its Applications to Real-Time Visual Measurement, J. of Robotics and Mechatronics, Vol.17, No.2, pp.121-129 (2005)
  www.scienova.com  
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Fig. 2 A continuous image sequence and zero level sets. Zero level sets, which are computed contours of our method, are drawn as white curves. A single paramecium is tracked to be in the middle of a visual field, even if another one swims into the visual field and contact with the tracked paramecium at 3000 ms.
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そこで我々は、以上の3性能を同時に向上する画像処理技術を発明した。①画像全体に対して評価関数を定義するRegion-Based (RB) LSMを定式化し、局所的なノイズにロバストにした。LSMは評価関数最小化の戦略で輪郭を初期値から更新し、対象を認識する画像処理である。②認識した細胞の光量がある一定値に近づくと評価関数が小さくなるBrightness-Limited (BL) LSMを定式化し、侵入問題と接触問題にロバストにした。一定値には、予め測定した光量の集団平均値を用いた。③上記2つのLSMを組み合わせたRB+BL LSMを並列（SIMD型プロセッサ）・高速(撮影速度1kHz)ビジョンシステムに実装することで、128×128画素8bitの白黒画像に対して、画像取得から輪郭認識迄を、細胞を見失わないmsオーダー（3ms以内、333Hz以上）で行えた。